# Indoor Air Quality Improvement

arXiv: https://arxiv.org/abs/2012.15387 · HTML: https://mlsanity.com/paper/2012.15387/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [ArchABM: an agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality](https://mlsanity.com/paper/2111.01484/) (2021)
- [Data Classification With Multiprocessing](https://mlsanity.com/paper/2312.15152/) (2023)
- [Beyond the Frame: Single and mutilple video summarization method with user-defined length](https://mlsanity.com/paper/2401.10254/) (2023)

## Abstract (arXiv)

Poor indoor air quality can contribute to the development of various chronic respiratory diseases such as asthma, heart disease, and lung cancer. Since air quality is extremely difficult for humans to detect though sensory processing, there is a need for efficient ventilation systems that can provide a healthier environment. In this paper, we have designed an energy efficient ventilation system that predicts sensor occupancy patterns based on historical data to improve indoor air quality.